Digital image correlation based internal friction characterization in granular materials
| Autor: | Ragunanth Venkatesh, Igor Emri, Edvard Govekar, Arkady Voloshin, Miha Brojan |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Spectrum analyzer
Digital image correlation Materials science granular materials Aerospace Engineering flowability 02 engineering and technology Granular material enoosno stiskanje 0203 mechanical engineering Newtonian fluid digital image correlation udc:531:62-492-026.772(045) Parametric statistics Mechanical Engineering uniaxial compression Internal pressure Mechanics 021001 nanoscience & nanotechnology 020303 mechanical engineering & transports Mechanics of Materials Solid mechanics pretočnost korelacija digitalne slike Particle size 0210 nano-technology internal friction notranje trenje zrnati materiali |
| Zdroj: | Experimental mechanics, vol. 60, no. 4, pp. 481-492, 2020. |
| ISSN: | 0014-4851 |
| Popis: | Based on the realization that Newtonian fluids have the unique property to redirect the forces applied to them in a perpendicular direction, a new apparatus, called the Granular Friction Analyzer (GFA), and the related GFA index, were proposed for characterizing the internal friction and related flow behavior of granular materials under uniaxial compression loading. The calculation of the GFA index is based on the integration of the internal pressure distribution along the cylinder wall, within which the granular material is being uniaxially compressed by a piston. In this paper an optical granular friction analyzer (O-GFA) is presented, where a digital image correlation (DIC) method is utilized to assess the cylinder strains used to calculate the internal pressure distribution. The main advantage of using the DIC method is that the starting point (piston–powder contact point) and the length of the integration considering the edge effects can be defined. By using the DIC full-field, instead of a few points strain measurements, a 2% improvement of the GFA index’s accuracy has been achieved and its robustness with respect to the number of points has been demonstrated. Using the parametric error analysis it has been shown that most of the observed total error (7.5%) arises from the DIC-method-based measurements of the strains, which can be improved by higher-resolution cameras and DIC algorithms for the strain evaluation. Additionally, it was shown that the GFA index can be used for determining the well-known Janssen model parameters. The latter was demonstrated experimentally, by testing three SS 316 L granular material samples with different mean particle sizes. The results confirm that the mean particle size regulates the internal friction of granular materials. |
| Databáze: | OpenAIRE |
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